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SOgym

Gym environment for developing structural optimization problems using deep reinforcement learning.

SOgym Logo

The environment is based on the topology optimization framework of Moving Morphable Components [1]. The design task is framed as a sequential decision process where at each timestep, the agent has to place one component.

Boundary Conditions

The environment samples from the following boundary conditions distribution at the start of each episode:

Boundary Conditions Visualization

Table 1: Parameters Defining the Boundary Conditions Distribution

Parameter Name Distribution
h Height [1.0, 2.0]
w Width [1.0, 2.0]
L_s Support Length 50% to 75%
P_s Support Position 0 to (100% of L_s)
P_L Load Position 0% to 100% of boundary opposite from support
θ_L Load Orientation [0°,360°] *

*The selected angle is filtered to ensure there is at least 45 degrees of difference with the support normal.

The blue wall represents a fully supported boundary and the red boundary the region where a unit load with varying orientation is randomly placed.

The environment's reward function can be modified to fit multiple constrained topology optimization objectives such as:

  • Compliance minimization under hard volume constraint [Implemented]
  • Compliance minimization under soft volume constraint [Implemented]
  • Compliance minimization under global/local stress constraint
  • Volume minimization under compliance constraint
  • Combined volume and compliance minimization

SOgym Leaderboard

Observation Space Configurations and Algorithms

Observation Space PPO SAC DreamerV3
Dense Result for PPO Result for SAC Result for DreamerV3
Image Result for PPO Result for SAC Result for DreamerV3
TopOpt Game Result for PPO Result for SAC Result for DreamerV3

Citation

To cite this library, please refer to the following paper:

Rochefort-Beaudoin, T., Vadean, A., Aage, N., & Achiche, S. (2024). Structural Design Through Reinforcement Learning. arXiv preprint arXiv:2407.07288.

References

[1] Zhang, W., Yuan, J., Zhang, J. et al. A new topology optimization approach based on Moving Morphable Components (MMC) and the ersatz material model. Struct Multidisc Optim 53, 1243–1260 (2016). https://doi.org/10.1007/s00158-015-1372-3

[2] Nobel-Jørgensen, Morten & Malmgren-Hansen, David & Bærentzen, Andreas & Sigmund, Ole & Aage, Niels. (2016). Improving topology optimization intuition through games. Structural and Multidisciplinary Optimization. 54. 10.1007/s00158-016-1443-0.